Statistical Techniques in Tableau Data Camp

Overview

Context

In this Statistical Techniques in Tableau course offered by Data Camp you will be using drag and drop analytics, you'll learn how to perform univariate and bivariate exploratory data analysis and create regression models to spot hidden trends.

Apply Machine Learning Techniques

Working with real-world datasets, you’ll also use machine learning techniques such as clustering and forecasting. It’s time to dig deeper into your data!

What you will do during this course:

• Exploratory data analysis, or EDA, is a fundamental step when doing data research. Getting the first insights of your data is easy in Tableau: you’ll be creating and interpreting tables, bar plots, histograms, and box plots in no time!
• You’ll dive deeper into the use of different measures of center and spread, and how they should be used in Tableau. You’ll learn about the use of the summary card, the difference between sample and population, and how variance, standard deviation, and confidence intervals can be calculated and visualized.
• It's time to look at two variables at a time. Describing the relationship between two variables, or regression, is a great way to spot trends in your data. You'll learn how to find the best trend line, describe the trend model, and predict future observations, using dinosaur data!
• You’ll explore two more advanced statistical techniques: forecasting and clustering. Forecasting helps you detect recurring patterns in your time-series data, and can predict how these patterns will change in the future. With clustering, you’re able to detect patterns in unlabeled data, allowing you to slice and dice your dataset to reveal hidden insights.